code: def lovasz_hinge_flat(logits, labels):
      """
      Binary Lovasz hinge loss
        logits: [P] Variable, logits at each prediction (between -\infty and +\infty)
        labels: [P] Tensor, binary ground truth labels (0 or 1)
        ignore: label to ignore
      """
      if len(labels) == 0:
          # only void pixels, the gradients should be 0
          return logits.sum() * 0.0
      signs = 2.0 * labels.float() - 1.0
      errors = 1.0 - logits * Variable(signs)
      errors_sorted, perm = torch.sort(errors, dim=0, descending=True)
      perm = perm.data
      gt_sorted = labels[perm]
      grad = lovasz_grad(gt_sorted)
      loss = torch.dot(F.relu(errors_sorted), Variable(grad))
      return loss
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import torch
  import torch.nn.functional
  from torch import nn
  from torch.autograd import Variable
label_desc: Use image node_id 5 for calling a global function with example usage: lovasz_hinge_flat(logits, labels) and returns: loss, unknown